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Fault detection with bayesian network

Abstract : The purpose of this chapter is to present a method for the fault detection in multivariate process, with a bayesian network. In this context, the detection is viewed as a classification task like the discriminant analysis, which can be transposed in a bayesian network. We prove mathematically the equivalence between the usual detection methods that are the multivariate control charts (Hotelling's T², MEWMA) and the quadratic discriminant analysis (in a bayesian network). So, this makes possible the fault detection with a bayesian network. An application on the Tennessee Eastman Process is given in order to demonstrate the approach.
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Submitted on : Monday, September 13, 2010 - 2:34:21 PM
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  • HAL Id : inria-00517063, version 1



Sylvain Verron, Teodor Tiplica, Abdessamad Kobi. Fault detection with bayesian network. Alexander Zemliak. Frontiers in Robotics, Automation and Control, IN-TECH, 2008, 9789537619176. ⟨inria-00517063⟩



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